In project Stadtpilot, described in, the object based environment perception system developed by the urban challenge team CarOLO at TU-Braunschweig, as presented in, was enhanced. In this new project context, the scenario is further complicated to include public traffic on large inner-city loops.
Other vehicles are described by the projects sensor data fusion by an open polyline (contour) with lots of points. Partially, these points lie on straight lines or represent noise of the contour, which do not contribute to the objects description. These extra points complicate an effective tracking and deform the contour of the object hypothesis. Because of the numerous traffic and due to the change in the environment’s type, surrounded vehicles create a change of view very often. This results in no or less measurement updates of some points in the contour and can result in deformation of the contour.
In an effort to overcome this problem, the contour estimating Kalman filter, presented in, was enhanced by improved point update algorithms as well as a contour classifier based upon evidence theory. These enhancements allow the decrease of the used points. Changes of view, due to passing traffic, are better identified because the classifier identifies the most likely shape explicitly.